Özet:

Machine learning techniques are used in many fields nowadays and allow us to classify data piles and extract useful information from data with predictive analysis. With developing technology, there is a significant increase in the number of recorded data in the field of health. With the machine learning methods, analysing and interpreting the data stacks in the field of health is important for the early diagnosis of many diseases. In this study, it is aimed to reach the algorithm that makes the most successful classification prediction on the data used in the study with machine learning algorithms in order to contribute to the early diagnosis of Cardiovascular Disease. The performances were compared by using five different machine learning methods; Naive Bayes, Logistic Regression, Random Forest, K-Nearest Neighbor and Support Vector Machines. The method that gives the most successful estimation performance has been determined. It is aimed to give an idea in the selection of the appropriate method from machine learning algorithms for the studies to be made on the prediction of a possible heart disease. At the same time, keeping up-to-date similar studies in the field of health can contribute to the early diagnosis and treatment of the disease.

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